Detection of Aspens Using High Resolution Aerial Laser Scanning Data and Digital Aerial Images

Säynäjoki, Raita; Packalén, Petteri; Maltamo, Matti; Vehmas, Mikko; Eerikäinen, Kalle

doi:10.3390/s8085037

Cited by 32 publications

(28 citation statements)

References 20 publications

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“…In a study by Säynäjoki et al (2008), single aspen trees were detected from dense ALS data. The inventory system was, however, rather complex, including, for instance, visual interpretations by aerial images to separate coniferous trees from deciduous trees.…”

Section: Introductionmentioning

confidence: 99%

Inventory of aspen trees in spruce dominated stands in conservation area

et al. 2015

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Background: The occurrence of aspen trees increases the conservation value of mature conifer dominated forests. Aspens typically occur as scattered individuals among major tree species, and therefore the inventory of aspens is challenging. Methods: We characterized aspen populations in a boreal nature reserve using diameter distribution, spatial pattern, and forest attributes: volume, number of aspens, number of large aspen stems and basal area median diameter. The data were collected from three separate forest stands in Koli National Park, eastern Finland. At each site, we measured breast height diameter and coordinates of each aspen. The comparison of inventory methods of aspens within the three stands was based on simulations with mapped field data. We mimicked stand level inventory by locating varying numbers of fixed area circular plots both systematically and randomly within the stands. Additionally, we also tested if the use of airborne laser scanning (ALS) data as auxiliary information would improve the accuracy of the stand level inventory by applying the probability proportional to size sampling to assist the selection of field plot locations. Results:The results showed that aspens were always clustered, and the diameter distributions indicated different stand structures in the three investigated forest stands. The reliability of the volume and number of large aspen trees varied from relative root mean square error figures above 50% with fewer sample plots (5-10) to values of 25%-50% with 10 or more sample plots. Stand level inventory estimates were also able to detect spatial pattern and the shape of the diameter distribution. In addition, ALS-based auxiliary information could be useful in guiding the inventories, but caution should be used when applying the ALS-supported inventory technique. Conclusions: This study characterized European aspen populations for the purposes of monitoring and management of boreal conservation areas. Our results suggest that if the number of sample plots is adequate, i.e. 10 or more stand level inventory will provide accurate enough forest attributes estimates in conservation areas (minimum accuracy requirement of RMSE% is 20%-50%). Even for the more ecologically valuable attributes, such as diameter distribution, spatial pattern and large aspens, the estimates are acceptable for conservation purposes.

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Section: Introductionmentioning

confidence: 99%

Inventory of aspen trees in spruce dominated stands in conservation area

et al. 2015

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“…in 2005 for another purpose, to develop ALS-based methods for the recognition of large aspens (Säynäjoki et al 2008). Site fertility on the various plots varied from fertile to relatively fertile.…”

Section: Kolimentioning

confidence: 99%

“…Hinsley et al (2002), for example, used ALS data to assess the quality of bird habitats, and Müller et al (2009) observed that ALS was superior to aerial photographs for estimating the abundance of birds in Southeastern Germany. In addition, ALS data have been used, for identifying large aspens (Säynäjoki et al 2008) and stands with high herbaceous plant diversity (Vehmas et al 2009). …”

Section: Remote Sensing and Its Application To The Assessment Of Ecolmentioning

confidence: 99%

Comparison of field inventory methods and use of airborne laser scanning for assessing coarse woody debris

Pesonen¹

2011

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“…Remote sensing can be used for measuring various environmental parameters, such as reflectance properties and three-dimensional (3D) structure of vegetation related to biodiversity (e.g., [23,24]). Reflectance properties capture variation between some of the tree species [25,26] as well as tree vigor [27,28]. Various remote sensing data sets (e.g., point clouds derived from airborne laser scanning (ALS) or optical imagery) enable characterizing forest Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

“…2018, 10, 338 3 of 22 structure through height, height variation, and density of the vegetation. ALS data have been used to map and monitor old deciduous trees within stands [26], stands with mixed species and multiple canopy layers [29][30][31][32], site type [33], as well as amount of dead wood [34], and canopy gaps [35], all being related to diversity of forest ecosystem. In addition, sparse ALS data was used by [36] to locate potential wildlife habitats, but concluded that information on shrub and herb layers, important habitat characteristics for game birds studied, was challenging.…”

Section: Introductionmentioning

confidence: 99%

Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

et al. 2018

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Forests are the most diverse terrestrial ecosystems and their biological diversity includes trees, but also other plants, animals, and micro-organisms. One-third of the forested land is in boreal zone; therefore, changes in biological diversity in boreal forests can shape biodiversity, even at global scale. Several forest attributes, including size variability, amount of dead wood, and tree species richness, can be applied in assessing biodiversity of a forest ecosystem. Remote sensing offers complimentary tool for traditional field measurements in mapping and monitoring forest biodiversity. Recent development of small unmanned aerial vehicles (UAVs) enable the detailed characterization of forest ecosystems through providing data with high spatial but also temporal resolution at reasonable costs. The objective here is to deepen the knowledge about assessment of plot-level biodiversity indicators in boreal forests with hyperspectral imagery and photogrammetric point clouds from a UAV. We applied individual tree crown approach (ITC) and semi-individual tree crown approach (semi-ITC) in estimating plot-level biodiversity indicators. Structural metrics from the photogrammetric point clouds were used together with either spectral features or vegetation indices derived from hyperspectral imagery. Biodiversity indicators like the amount of dead wood and species richness were mainly underestimated with UAV-based hyperspectral imagery and photogrammetric point clouds. Indicators of structural variability (i.e., standard deviation in diameter-at-breast height and tree height) were the most accurately estimated biodiversity indicators with relative RMSE between 24.4% and 29.3% with semi-ITC. The largest relative errors occurred for predicting deciduous trees (especially aspen and alder), partly due to their small amount within the study area. Thus, especially the structural diversity was reliably predicted by integrating the three-dimensional and spectral datasets of UAV-based point clouds and hyperspectral imaging, and can therefore be further utilized in ecological studies, such as biodiversity monitoring.

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Detection of Aspens Using High Resolution Aerial Laser Scanning Data and Digital Aerial Images

Cited by 32 publications

References 20 publications

Inventory of aspen trees in spruce dominated stands in conservation area

Inventory of aspen trees in spruce dominated stands in conservation area

Comparison of field inventory methods and use of airborne laser scanning for assessing coarse woody debris

Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

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